How AI Enhances Biotech Operations

AI in biotechnology can improve drug development from candidate screening to trial management.

FREMONT, CA: Artificial intelligence (AI) is futuristic, and it exists in several everyday technologies. AI offers handheld devices voice and facial recognition potentials. AI is making its presence felt in the biotechnology industry, where it has become critical to various aspects of drug discovery and development. AI applications in biotech are drug target identification, image screening, drug screening, and predictive modeling. AI is also being leveraged to comb through the scientific literature and handle clinical trial data. AI can manage clinical trial datasets, allow virtual screening, and analyze massive amounts of data. Besides mitigating clinical trial costs, AI can get unobtainable insights and feed them back into drug development operations. Read on to know more.

Machine learning provides exciting opportunities in the realm of clinical diagnostics. AI technologies for medical applications use AI to identify disease from retinal images. In clinical trial medical centers around the United States, sensitivity for identifying diabetic retinopathy was over 95 per cent. Conventional methods of data analysis in drug discovery work well with homogenous data.

 However, these methods fall short when the data is complex when patient records chronicle several diagnoses, comorbidities, complex treatment plans, and clinics and clinicians' encounters. AI can combine that information, analyze it, and create stratified patient groups. That potential to handle complex data is revolutionizing the design and execution of clinical trials.

Some firms use AI and machine learning to unearth how patients respond to treatments in real-world settings. The firm's work can guide pharmaceutical research, inform outcomes research and value-based studies, and accelerate drug development. AI can also be leveraged to make predictions, like whether a patient will be a responder or whether the response is durabile. These predictions can be good in the design of a clinical study. And they can provide clinical researchers with a direct line of sight into the standard of care, which is essential because often physicians will not place patients in a trial if it is too much of a burden compared to the standard of care.

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